Embodiments of the present specification relate to detection of rare failure events, and more particularly to optimized systems and methods for determining a rare failure event in complex socio-technical systems.
Heterogeneous engineering systems having both technical infrastructure such as hardware and social infrastructure such as agents and institutions are generally referred to as socio-technical systems. By way of example, heterogeneous systems such as aircraft management systems and power network management systems include a close interplay of diverse technical artifacts and social artifacts. An air traffic management system includes artifacts such as a luggage handling system, a runway, a control center, an airplane, and a passenger booking system. Similarly, a power network management system includes a distributed physical network, power generation systems, and social organizations such as power trading entities. Management of socio-technical systems requires efficient modelling of both the technical artifacts and social artifacts. The design methodology for these socio-technical systems requires a rigorous performance analysis approach based on such models. A failure probability measure is an important parameter representative of an overall performance of the socio-technical systems.
Multi-agent dynamic risk models (MA-DRMs) have been successfully used in the analysis of complex socio-technical systems such as a fleet of aircrafts operated by a group of pilots. In particular, the MA-DRMs have been used to determine a probability of failure events in the complex socio-technical systems. Typically, failure in a critical complex socio-technical system is a rare event having a measure of one in a billion or more opportunities. Traditionally, simulation techniques such as Monte Carlo methods have been used for estimating failure and accident rates. However, the computational complexity of the conventional simulation methods for analyzing socio-technical systems is very high. Sequential Monte Carlo methods that entail use of simulation based optimization techniques are performed in an iterative manner to reduce the computational complexity. The sequential Monte Carlo methods applied to a Markov process have enhanced capability of detecting of rare failure events. However, these methods may degenerate after a few successive re-sampling steps. Degeneration is manifested in high variance, lack of diversity or in failure to obtain the desired event.